This report is focused around Lost and Found data using the intakes and outcomes data received for 2019-2021 (up to September). Its goal is to reflect everything we could learn about L&F from the available data, make sure the numbers we see make sense, and highlight things that would be useful to show but some/all data required for them are missing.

Date range: 2019-01-01 to 2021-09-30

Report Structure

  1. KPIs: data points that indicate how good the shelter is doing on on L&F. They have numeric goals associated with them.
  2. Supporting data: data points that aren’t a goal themselves but serve as a proxy for improving a goal. For example, the method of RTH is not a performance indicator, but it helps identifying how RTHs take place. The number of strays found per ZIP code is not a metric to improve, but it shows where most strays are coming from to guide resource allocation.
  3. Data notes: the state of the data received from the shelter.
  4. Extra metrics: some ideas for additional L&F metrics and the data points they require.

Scroll down or use the table of contents on the left to navigate throughout the document. Most sections contain multiple tabs showing different facets of a data type. Most plots are interactive, meaning they include tooltips and allow hiding and showing parts and zooming in and out. If something went wrong, look for the house icon in the top right corner of each figure to reset.

KPIs

Yearly RTH Rates by Species

This table covers all strays and RTHs. RTH rates shown below are the number of strays with RTH outcomes out of all strays.

When we go over this, let’s make sure we calculate the rate the same way you do, so we would want to make sure what we see makes sense. If these numbers are right, the 2021 figure is higher than and HASS averages for dogs which is at 30% RTH rate and about the same as the RTH rate for cats.

Normally, we would make versions of this table for the main subsets of stray intake such as field and public drop offs, but since you document them under “intake source/route” as opposed to subtype (as is more standard) this would take a bit more preprocessing. We’re happy to do that if that’s of interest.

Species Year Strays RTH_Count RTH_Rate
Cat 2019 3335 40 1.2%
Cat 2020 2000 40 2%
Cat 2021 2255 40 1.77%
Dog 2019 1803 463 25.68%
Dog 2020 892 227 25.45%
Dog 2021 1129 405 35.87%

RTH Over Time

These three time series show the RTH rate per month, to show whether there were times with particularly high or low rates as well as the overall trajectory. Similar to the previous section, we can later plot different lines for field and OTC intakes.

It seems like there was a significant decrease in RTH rates (for dogs) from June 2020 (affected by the pandemic?), but starting February 2021 the rates have climbed back and surpassed pre-pandemic ones!

Stray Intakes

This section shows the number of stray intakes over time, as well as the breakdown of strays by field/shelter intake, primarily to give us some context for the rest of the data seeing how many animals are coming in. It is probably not anything you don’t already know.

Stray Intakes by Month

Stray Intake Subtypes

Length of Stay Differences - RTH v. Other Outcomes

The average difference in length of stay (in days) between strays with RTH outcomes and all other strays is shown in the table below – roughly 20 days for dogs and 24 for cats. That means that every successful RTH saves 24 days of care on average (for dogs) at Michigan.

We can make a cost savings calculation using these LOS numbers, the number of RTHs, and a daily cost of care if that is of interest. To offer a simple example, assuming a daily cost of care of $30, returning an extra 200 dogs in 2021 would have resulted in a $120,000 cost saving.

Species Outcome Count Average_Length_Of_Stay
Cat Other Outcomes 6872 28.21
Cat RTO 120 4.48
Dog Other Outcomes 2558 22.87
Dog RTO 1095 2.51

Supporting Data

Stray Intake and RTH By Found ZIP

The following maps show stray intake and RTH rate by ZIP codes to highlight geographical patterns. The first and second tab are similar to previous metrics; the third tab, RTH Gap, shows the number of strays who were not returned home per ZIP code.

*Note: ideally, we would use found locations / crossing to map stray intakes as they allow greater specificity. However, they were unavailable (we’re happy to make new maps upon receiving those). Specific addresses also help us exclude animals who have the shelter address as their found location, which might make the shelter’s ZIP code stand out erroneously (and it does stand out, as you’ll see below). Take that into consideration when looking at the maps.

Stray Intake - Dogs

RTH Rate

RTH Gap

This combines the other two tabs to highlight where most additional RTH potential exists. As the RTH rate is uniformly high across the city, the areas with more stray intakes stand out.

Stray Intake and RTH By Found Location - Cats

This is similar to the maps above, but for cats. Similar areas stand out to the dogs map, particularly the shelter’s zip code.

Stray Intake

RTH Rate

Since RTH rate is pretty low across the city, it is also low throughout in this map.

RTH Gap

This is very similar to the stray map because of the low RTH occurrence for cats.

Showing 5689 stray cats of which 116 were RTH.

Microchip Analysis

In this version, we did not have microchip information yet, but since the subtype fields are used to separate animals with/out ids, we used this field to look at ID prevalence and how it affects RTH rates.

How many animals come in with an ID?

The following table breaks it down by species. There are more dogs and coming in IDed (8.5%) than cats (1.8%), but both percentages are low.

Species Identification Count Ratio
Cat FALSE 7387 97.3%
Cat TRUE 140 1.8%
Cat NA 63 0.8%
Dog FALSE 3493 91.3%
Dog TRUE 326 8.5%
Dog NA 5 0.1%
Other FALSE 237 95.2%
Other TRUE 12 4.8%

RTH Rate with/out an ID

This comparison is stronger after also making sure animals compared are similar on other characteristics, such as intake condition and age. But to get a first impression, for cats the RTH rate with chips is 18% compared to 1% without one, whereas for dogs, there is a 69% RTH rate for dogs with IDs vs 25% without chips.

The difference is obviously high, but it is worth also thinking about what might make the ‘yes’ category be at 69% as opposed to 100% (since there is presumably an owner), such as owners refusing, fees, wrong details on the chip, etc.

Species Identification Strays RTH_Count RTH_Rate
Cat FALSE 7387 94 1%
Cat TRUE 140 25 18%
Cat NA 63 1 2%
Dog FALSE 3493 870 25%
Dog TRUE 326 225 69%
Other FALSE 237 9 4%

ID Prevalence - Mapping

This section shows the ID rate (% of animals who came in with a chip) from each Census tract, as well as the number of animals coming with without an ID from each Census tract.

the following map shows the proportion of animals who came with an ID. It is interesting that there are such few areas in which animals are found with IDs, mostly around the 48185 area.

Data Notes

  1. Zip codes - 72 of the 11663 stray animals did not have a ZIP code listed.

  2. Found locations were missing for all animals.

  3. Intake Subtype - as noted, we could make a version that would take into acount the source/reason data for field/OTC separation.

  4. Outcome Subtype for RTH - if a return to owner in the field can take place, it is worth creating a dedicated outcome subtype for it.

Outcome_Subtype N
Stray Reclaim 692
NA 507
Surrender RTO 18
RTO 4
Return to Owner 2
Redemption / Returned to Owner 1

Extra Metrics

Other things we could show if we had the data for it:

  1. Distances traveled by lost dogs from home, if home address is collected for successful RTH.
  2. Differentiating field / OTC animals.
  3. Tracking outcomes of chip follow-ups to figure out the gap of chipped but non-RTH animals.
  4. Mapping by census tract and layering of human demographic data on top of shelter data.
  5. Number of public found reports and successful RTH by the public (if this data is accessible to the shelter).

Thanks for reading through, and we’re looking forward to talking through it and thinking about more ways to make this data useful for you.